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library(xgboost)

载入程辑包：‘xgboost’

The following object is masked from ‘package:IRanges’:

    slice

The following object is masked from ‘package:plotly’:

    slice

The following object is masked from ‘package:dplyr’:

    slice

library(Matrix)

载入程辑包：‘Matrix’

The following object is masked from ‘package:S4Vectors’:

    expand

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack

library(mclust)

    __  ___________    __  _____________
   /  |/  / ____/ /   / / / / ___/_  __/
  / /|_/ / /   / /   / / / /\__ \ / /   
 / /  / / /___/ /___/ /_/ /___/ // /    
/_/  /_/\____/_____/\____//____//_/    version 5.4.9
Type 'citation("mclust")' for citing this R package in publications.

ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")

分发训练集

Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3061
Number of edges: 99234

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9566
Number of communities: 4
Elapsed time: 0 seconds

ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 6498
Number of edges: 215869

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9332
Number of communities: 3
Elapsed time: 0 seconds

umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)

Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Warning: Removed 113 rows containing missing values (geom_point).

umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)

Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Warning: Removed 19 rows containing missing values (geom_point).

# AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
# PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)

在AC上预训练

AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

# data(agaricus.train, package='xgboost')

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

xgboost_roc[["auc"]] #只需要这个值

Multi-class area under the curve: 0.9883

adjustedRandIndex(AC_test_data$label, predict_AC_test) #分类器性能

[1] 0.9585312

在PA上训练

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)

xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC

Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases

xgboost_roc[["auc"]]

Multi-class area under the curve: 0.9635

adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能

[1] 0.8821278

选择特征common genes of top 500

使用所有来自PA的细胞训练分类器

应用在AC上，计算ARI

selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)
write.csv(selected_features, "./datatable/selected_features.csv", row.names = F)

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)

xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()

write.csv(importance, "./datatable/PAtrain_features.csv", row.names = F)

# multi_featureplot(head(importance,9)$Feature, ds2)

应用到AC上

xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC

Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases

xgboost_roc[["auc"]]

Multi-class area under the curve: 0.8273

# multi_featureplot(head(importance,4)$Feature, ds2, split.by = "conditions")
# multi_featureplot(head(importance,9)$Feature, ds2_AC)

# 计算ARI 
adjustedRandIndex(predict_AC_test, AC_test_data$label)

[1] 0.3024837

umapplot(ds2,split.by = "conditions")
table(ds2$conditions)

sankey plot

PA -> AC ARI 0.3024837

    pre
true           0           1           2
   0 0.980360065 0.003273322 0.016366612
   1 0.799516908 0.196859903 0.003623188
   2 0.453004622 0.493066256 0.053929122
   3 0.002762431 0.052486188 0.944751381

AC -> PA ARI 0.1797689

    pre
true           0           1           2           3
   0 0.027107438 0.287272727 0.682644628 0.002975207
   1 0.000349895 0.075227432 0.914975507 0.009447166
   2 0.008130081 0.003252033 0.175609756 0.813008130

varify 部分

病变程度量化 # 数据集CA_dataset1 ## 在dataset2的AC上训练 使用top500 in AC, 7:3分发训练集

temp <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))

for(i in intersect(AC_genes$Feature, rownames(temp))){
  AC_data[i,] <- temp[i,]
}
rm(temp)
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

AC_confuse_matrix_test_prop

    pre
true           0           1           2           3
   0 0.994652406 0.000000000 0.005347594 0.000000000
   1 0.000000000 0.987755102 0.012244898 0.000000000
   2 0.000000000 0.021164021 0.968253968 0.010582011
   3 0.000000000 0.000000000 0.045045045 0.954954955

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC

Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases

xgboost_roc[["auc"]]

Multi-class area under the curve: 0.9913

# 计算ARI 
adjustedRandIndex(predict_AC_test, AC_test_data$label)

[1] 0.9648073

temp <- get_data_table(ds1, highvar = F, type = "data")
ds1_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))
for(i in intersect(AC_genes$Feature, rownames(temp))){
  ds1_data[i,] <- temp[i,]
}
rm(temp)
ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL
ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)
ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#计算混淆矩阵
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)

x <- c("ds1_0", "ds1_1", "ds1_2", "ds1_3")
y <- c("AC_0", "AC_1", "AC_2", "AC_3")

prop <- as.numeric(ds1_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()

ggsave("./plots/ACmodel_dataset1.png", plot = plot, device = png, width = 5,height = 4)


ds1_data_confuse_matrix_test

    pre
true    0    1    2    3
   0    0   45 2516    8
   1    4   94  787    7
   2    0    6  474   10
   3    0    1   50  347

ds1_data_confuse_matrix_test_prop  #分析发育轨迹

    pre
true           0           1           2           3
   0 0.000000000 0.017516543 0.979369404 0.003114052
   1 0.004484305 0.105381166 0.882286996 0.007847534
   2 0.000000000 0.012244898 0.967346939 0.020408163
   3 0.000000000 0.002512563 0.125628141 0.871859296

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC

Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases

xgboost_roc[["auc"]]

Multi-class area under the curve: 0.7198

# 计算ARI 
adjustedRandIndex(predict_ds1_test, ds1_test_data$label)

[1] 0.2321442

冠状动脉数据集

ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5401
Number of edges: 173943

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9544
Number of communities: 5
Elapsed time: 0 seconds

umapplot(ds0)

f("TAGLN",ds0)

# ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.7, min.diff.pct = 0.2)

selected_features <- AC_genes$Feature
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features), 
                   ncol = length(colnames(temp)), byrow = FALSE, 
                   dimnames = list(selected_features,colnames(temp)))
for(i in intersect(selected_features,rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
rm(temp)

ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))

ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)

x <- c("ds0_0", "ds0_1", "ds0_2", "ds0_3", "ds0_4")
y <- c("AC_0", "AC_1", "AC_2")

prop <- as.numeric(ds0_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/ACmodel_humancor.png", plot = plot, device = png, width = 5,height = 4)

ds0_data_confuse_matrix_test

    pre
true    0    1    2
   0 1997    2  179
   1   98  156 1531
   2    3 1208    2
   3  173    0    0
   4   52    0    0

ds0_data_confuse_matrix_test_prop  #分析发育轨迹

    pre
true            0            1            2
   0 0.9168962351 0.0009182736 0.0821854913
   1 0.0549019608 0.0873949580 0.8577030812
   2 0.0024732069 0.9958779885 0.0016488046
   3 1.0000000000 0.0000000000 0.0000000000
   4 1.0000000000 0.0000000000 0.0000000000

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC

Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls < cases

# 计算ARI 
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)

[1] 0.7047121

labels <- lapply(levels(Idents(ds2_AC)), paste0, "_AC") %>% as.character()
labels2 <- lapply(levels(Idents(ds0)), paste0, "_ds0") %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2))  #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(ds0_data_confuse_matrix_test),
      color = colors
      ))

# load("./init.RData")
multi_featureplot(head(importance2,9)$Feature, ds2_AC)
multi_featureplot(head(importance2,9)$Feature, ds0)
multi_featureplot(head(importance2,9)$Feature, ds1)
f("MYH11", ds2_AC)
umapplot(ds0)

淋巴细胞

Idents(lym_ds2) <- lym_ds2$conditions
lym_ds2_AC <- subset(lym_ds2, idents = "AC")
lym_ds2_PA <- subset(lym_ds2, idents = "PA")
lym_ds2_AC <- lym_ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)

Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2990
Number of edges: 98189

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9128
Number of communities: 5
Elapsed time: 0 seconds

umapplot(lym_ds2_AC)

lym_ds2_PA <- lym_ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)

Computing nearest neighbor graph
Computing SNN

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 13746
Number of edges: 456548

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9116
Number of communities: 6
Elapsed time: 1 seconds

umapplot(lym_ds2_PA)

用PA的lym训练

lym_PA_data <- get_data_table(lym_ds2_PA, highvar = F, type = "data")
lym_PA_label <- as.numeric(as.character(Idents(lym_ds2_PA)))

set.seed(7)
index <- c(1:dim(lym_PA_data)[2]) %>% sample(ceiling(0.3*dim(lym_PA_data)[2]), replace = F, prob = NULL)
colnames(lym_PA_data) <- NULL
lym_PA_train_data <- list(data = t(as(lym_PA_data[,-index],"dgCMatrix")), label = lym_PA_label[-index])
lym_PA_test_data <- list(data = t(as(lym_PA_data[,index],"dgCMatrix")), label = lym_PA_label[index])

lym_PA_train <- xgb.DMatrix(data = lym_PA_train_data$data,label = lym_PA_train_data$label)
lym_PA_test <- xgb.DMatrix(data = lym_PA_test_data$data,label = lym_PA_test_data$label)

watchlist <- list(train = lym_PA_train, eval = lym_PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(lym_ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, lym_PA_train, nrounds = 200, watchlist, verbose = 1)

# 特征提取
importance <- xgb.importance(colnames(lym_PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)+theme_bw() #这个cluster和分类没有关系
lym_PA_genes <- head(importance, 500) ##选择top500

write.csv(lym_PA_genes,"./datatable/lym_PA_features.csv", row.names = F)
#混淆矩阵
predict_lym_PA_test <- round(predict(bst_model, newdata = lym_PA_test))

lym_PA_confuse_matrix_test <- table(lym_PA_test_data$label, predict_lym_PA_test, dnn=c("true","pre"))
lym_PA_confuse_matrix_test_prop <- prop.table(lym_PA_confuse_matrix_test, 1)
lym_PA_confuse_matrix_test_prop

x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")

prop <- as.numeric(lym_PA_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel.png", plot = plot, device = png, width = 7,height =6)

用AC的lym验证

xgboost_roc[["auc"]]

Multi-class area under the curve: 0.7198

adjustedRandIndex(predict_lym_AC_test, lym_AC_test_data$label)

[1] 0.7227654

lym_AC_confuse_matrix_test_prop

    pre
true           0           1           2           3           4           5
   0 0.794649882 0.004720692 0.000000000 0.200629426 0.000000000 0.000000000
   1 0.000000000 0.030172414 0.897988506 0.000000000 0.071839080 0.000000000
   2 0.130268199 0.829501916 0.032567050 0.003831418 0.000000000 0.003831418
   3 0.000000000 0.041841004 0.000000000 0.000000000 0.958158996 0.000000000
   4 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000

labels <- lapply(levels(Idents(lym_ds2_PA)), paste0, "_lymPA") %>% as.character()
labels2 <- lapply(levels(Idents(lym_ds2_AC)), paste0, "_lymAC") %>% as.character()
sources <- rep(0:5, each = 5)  #注意这里的each和times的区别
colors <- rep(colors_list[1:6], each = 5)
targets <- rep(6:10, times = 6)

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(lym_AC_confuse_matrix_test),
      color = colors
      ))

umapplot(lym_ds2_AC)

umapplot(lym_ds2_PA)

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